{"title":"A framework for understanding event abstraction problem solving: Current states of event abstraction studies","authors":"","doi":"10.1016/j.datak.2024.102352","DOIUrl":null,"url":null,"abstract":"<div><p>Event abstraction is a crucial step in applying process mining in real-world scenarios. However, practitioners often face challenges in selecting relevant research for their specific needs. To address this, we present a comprehensive framework for understanding event abstraction, comprising four key components: event abstraction sub-problems, consideration of process properties, data types for event abstraction, and various approaches to event abstraction. By systematically examining these components, practitioners can efficiently identify research that aligns with their requirements. Additionally, we analyze existing studies using this framework to provide practitioners with a clearer view of current research and suggest expanded applications of existing methods.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data & Knowledge Engineering","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169023X24000764","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Event abstraction is a crucial step in applying process mining in real-world scenarios. However, practitioners often face challenges in selecting relevant research for their specific needs. To address this, we present a comprehensive framework for understanding event abstraction, comprising four key components: event abstraction sub-problems, consideration of process properties, data types for event abstraction, and various approaches to event abstraction. By systematically examining these components, practitioners can efficiently identify research that aligns with their requirements. Additionally, we analyze existing studies using this framework to provide practitioners with a clearer view of current research and suggest expanded applications of existing methods.
期刊介绍：
Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKE reaches a world-wide audience of researchers, designers, managers and users. The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems.
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